Non-Local Attention: When CNN-based methods conduct convolution in a local receptive field, the contextual information outside this field is ignored, while the features in distant regions may have a high correlation and can provide effective information. Given this issue, non-local attention has been proposed as a filtering algorithm to compute a weighted mean of all pixels of an image. In this way, distant pixels can also contribute to the response of a position in concern. For example, the non-local operation is conducted in a limited neighborhood to improve the robustness in NLRN [1]}. A non-local attention block is proposed in RNAN [2]}, where the attention mechanisms in both channel- and spatial-wise are used simultaneously in its mask branch to better guide feature extraction in the trunk branch. Meanwhile, a holistic attention network is proposed in HAN [3]}, which consists of a layer attention module and a channel-spatial attention module, to model the holistic interdependence among layers, channels, and positions. In CSNLN [4]}, a cross-scale non-local attention module is proposed to mine long-range dependencies between LR features and large-scale HR patches within the same feature map. All these methods show the effectiveness of the non-local attention, which can further improve the model performance.
